Umer Saeed
Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review
Saeed, Umer; Shah, Syed Yaseen; Ahmad, Jawad; Imran, Muhammad Ali; Abbasi, Qammer H.; Shah, Syed Aziz
Authors
Syed Yaseen Shah
Dr Jawad Ahmad J.Ahmad@napier.ac.uk
Visiting Lecturer
Muhammad Ali Imran
Qammer H. Abbasi
Syed Aziz Shah
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.
Citation
Saeed, U., Shah, S. Y., Ahmad, J., Imran, M. A., Abbasi, Q. H., & Shah, S. A. (2022). Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review. Journal of Pharmaceutical Analysis, 12(2), 193-204. https://doi.org/10.1016/j.jpha.2021.12.006
Journal Article Type | Review |
---|---|
Acceptance Date | Dec 30, 2021 |
Online Publication Date | Jan 5, 2022 |
Publication Date | 2022-04 |
Deposit Date | Jul 15, 2022 |
Publicly Available Date | Jul 15, 2022 |
Journal | Journal of Pharmaceutical Analysis |
Print ISSN | 2095-1779 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Issue | 2 |
Pages | 193-204 |
DOI | https://doi.org/10.1016/j.jpha.2021.12.006 |
Keywords | Artificial intelligence, Non-invasive healthcare, Machine learning, Non-contact sensing, COVID-19 |
Public URL | http://researchrepository.napier.ac.uk/Output/2889069 |
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Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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